Viz_and_eda
2025-09-26
Import the weather data.
data("weather_df")Making our first plot
ggplot(data=weather_df, mapping=aes(x=tmin,y=tmax)) +
geom_point()## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
Another way to do it is through the pipe function
weather_df %>%
ggplot(aes(x=tmin,y=tmax)) +
geom_point()## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggp_weather_scatterplot=
weather_df %>%
ggplot(aes(x=tmin,y=tmax)) +
geom_point()
ggp_weather_scatterplot## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
Check that some rows are missing.
weather_df %>%
filter(is.na(tmax))## # A tibble: 17 × 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 Molokai_HI USW00022534 2022-05-31 NA NA NA
## 2 Waterhole_WA USS0023B17S 2021-03-09 NA NA NA
## 3 Waterhole_WA USS0023B17S 2021-12-07 51 NA NA
## 4 Waterhole_WA USS0023B17S 2021-12-31 0 NA NA
## 5 Waterhole_WA USS0023B17S 2022-02-03 0 NA NA
## 6 Waterhole_WA USS0023B17S 2022-08-09 NA NA NA
## 7 Waterhole_WA USS0023B17S 2022-08-10 NA NA NA
## 8 Waterhole_WA USS0023B17S 2022-08-11 NA NA NA
## 9 Waterhole_WA USS0023B17S 2022-08-12 NA NA NA
## 10 Waterhole_WA USS0023B17S 2022-08-13 NA NA NA
## 11 Waterhole_WA USS0023B17S 2022-08-14 NA NA NA
## 12 Waterhole_WA USS0023B17S 2022-08-15 NA NA NA
## 13 Waterhole_WA USS0023B17S 2022-08-16 NA NA NA
## 14 Waterhole_WA USS0023B17S 2022-08-17 NA NA NA
## 15 Waterhole_WA USS0023B17S 2022-08-18 NA NA NA
## 16 Waterhole_WA USS0023B17S 2022-08-19 NA NA NA
## 17 Waterhole_WA USS0023B17S 2022-12-31 76 NA NAFancier scatterplotes !
Note that aes is for mapping the points alpha sets the opacity
weather_df %>%
ggplot(aes(x=tmin,y=tmax,color=name)) +
geom_point(alpha=0.3)+
geom_smooth(se=FALSE)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
weather_df## # A tibble: 2,190 × 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2021-01-01 157 4.4 0.6
## 2 CentralPark_NY USW00094728 2021-01-02 13 10.6 2.2
## 3 CentralPark_NY USW00094728 2021-01-03 56 3.3 1.1
## 4 CentralPark_NY USW00094728 2021-01-04 5 6.1 1.7
## 5 CentralPark_NY USW00094728 2021-01-05 0 5.6 2.2
## 6 CentralPark_NY USW00094728 2021-01-06 0 5 1.1
## 7 CentralPark_NY USW00094728 2021-01-07 0 5 -1
## 8 CentralPark_NY USW00094728 2021-01-08 0 2.8 -2.7
## 9 CentralPark_NY USW00094728 2021-01-09 0 2.8 -4.3
## 10 CentralPark_NY USW00094728 2021-01-10 0 5 -1.6
## # ℹ 2,180 more rowsWhere you define aesthetics can matter
weather_df %>%
ggplot(aes(x=tmin,y=tmax)) +
geom_point(aes(color=name, size=prcp),alpha=0.3)+
geom_smooth(se=FALSE)## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 19 rows containing missing values or values outside the scale range
## (`geom_point()`).
weather_df## # A tibble: 2,190 × 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2021-01-01 157 4.4 0.6
## 2 CentralPark_NY USW00094728 2021-01-02 13 10.6 2.2
## 3 CentralPark_NY USW00094728 2021-01-03 56 3.3 1.1
## 4 CentralPark_NY USW00094728 2021-01-04 5 6.1 1.7
## 5 CentralPark_NY USW00094728 2021-01-05 0 5.6 2.2
## 6 CentralPark_NY USW00094728 2021-01-06 0 5 1.1
## 7 CentralPark_NY USW00094728 2021-01-07 0 5 -1
## 8 CentralPark_NY USW00094728 2021-01-08 0 2.8 -2.7
## 9 CentralPark_NY USW00094728 2021-01-09 0 2.8 -4.3
## 10 CentralPark_NY USW00094728 2021-01-10 0 5 -1.6
## # ℹ 2,180 more rowsUse facetting real quick: works by making multiple plots in a row to column framework
weather_df %>%
ggplot(aes(x=tmin,y=tmax)) +
geom_point(aes(color=name),alpha=0.3,size=0.3)+
geom_smooth(se=FALSE)+
facet_grid(.~name)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
weather_df## # A tibble: 2,190 × 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2021-01-01 157 4.4 0.6
## 2 CentralPark_NY USW00094728 2021-01-02 13 10.6 2.2
## 3 CentralPark_NY USW00094728 2021-01-03 56 3.3 1.1
## 4 CentralPark_NY USW00094728 2021-01-04 5 6.1 1.7
## 5 CentralPark_NY USW00094728 2021-01-05 0 5.6 2.2
## 6 CentralPark_NY USW00094728 2021-01-06 0 5 1.1
## 7 CentralPark_NY USW00094728 2021-01-07 0 5 -1
## 8 CentralPark_NY USW00094728 2021-01-08 0 2.8 -2.7
## 9 CentralPark_NY USW00094728 2021-01-09 0 2.8 -4.3
## 10 CentralPark_NY USW00094728 2021-01-10 0 5 -1.6
## # ℹ 2,180 more rowsweather_df %>%
ggplot(aes(x=tmin,y=tmax)) +
geom_point(aes(color=name),alpha=0.3,size=0.3)+
geom_smooth(se=FALSE)+
facet_grid(name~.)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
weather_df## # A tibble: 2,190 × 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2021-01-01 157 4.4 0.6
## 2 CentralPark_NY USW00094728 2021-01-02 13 10.6 2.2
## 3 CentralPark_NY USW00094728 2021-01-03 56 3.3 1.1
## 4 CentralPark_NY USW00094728 2021-01-04 5 6.1 1.7
## 5 CentralPark_NY USW00094728 2021-01-05 0 5.6 2.2
## 6 CentralPark_NY USW00094728 2021-01-06 0 5 1.1
## 7 CentralPark_NY USW00094728 2021-01-07 0 5 -1
## 8 CentralPark_NY USW00094728 2021-01-08 0 2.8 -2.7
## 9 CentralPark_NY USW00094728 2021-01-09 0 2.8 -4.3
## 10 CentralPark_NY USW00094728 2021-01-10 0 5 -1.6
## # ℹ 2,180 more rowsLets make a somewhat more interesting scatterplot
weather_df %>%
ggplot(aes(x=tmin,y=tmax,color=name, shape=name)) +
geom_point(aes(size=prcp),alpha=0.3)+
geom_smooth(se=FALSE)+
facet_grid(.~name)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 19 rows containing missing values or values outside the scale range
## (`geom_point()`).
Learning Assessment: Write a code chain that starts with weather_df; focuses only on Central Park, converts temperatures to Fahrenheit, makes a scatterplot of min vs. max temperature, and overlays a linear regression line (using options in geom_smooth())
This R code is taking a dataset of weather observations and producing
a scatterplot of daily minimum versus maximum temperatures for Central
Park, New York. It begins with weather_df and uses the pipe
operator %>% to pass the data through a sequence of
transformations. The first step filters the dataset so that only rows
where the variable name equals
"CentralPark_NY" remain. Next, mutate()
creates two new variables—tmax_fahr and
tmin_fahr—by converting the maximum and minimum daily
temperatures from Celsius to Fahrenheit using the formula °F = (°C ×
9/5) + 32. With these new variables in place, the code calls
ggplot() to set up a graph in which tmin_fahr is mapped to
the x-axis and tmax_fahr to the y-axis. The
geom_point(alpha = 0.5) layer then plots the data as
semi-transparent points, which helps visualize overlapping values.
Finally, geom_smooth(method = "lm", se = FALSE) adds a
linear regression line to the plot without showing the shaded confidence
interval.
weather_df %>%
filter(name=="CentralPark_NY") %>%
mutate(
tmax_fahr=tmax*(9/5)+32,
tmin_fahr=tmin*(9/5)+32) %>%
ggplot(aes(x=tmin_fahr,y=tmax_fahr))+
geom_point(alpha=0.5)+
geom_smooth(method = "lm",se=FALSE)## `geom_smooth()` using formula = 'y ~ x'
##Small things
weather_df %>%
ggplot(aes(x=tmin,y=tmax,color=name,shape=name))+
#geom_point(alpha=0.3)+
geom_smooth(se=FALSE)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).
weather_df %>%
ggplot(aes(x=tmin,y=tmax,color=name,shape=name))+
geom_smooth(se=FALSE)+
geom_point()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
weather_df %>%
ggplot(aes(x=tmin,y=tmax))+
geom_hex()## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_binhex()`).
weather_df %>%
ggplot(aes(x=tmin,y=tmax))+
geom_point(color="green")## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
##Univariate plots
weather_df %>%
ggplot(aes(x=tmin))+
geom_histogram(color="white",fill="green")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_bin()`).
weather_df %>%
ggplot(aes(x=tmin,fill=name))+
geom_histogram()+
facet_grid(name~.)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_bin()`).
maybe a density plot ?
weather_df %>%
ggplot(aes(x=tmin,fill=name))+
geom_density(alpha=0.2)## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_density()`).
Box plot
weather_df %>%
ggplot(aes(x=name,y=tmin))+
geom_boxplot(aes(fill=name))## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
violin plots
weather_df %>%
ggplot(aes(x=name,y=tmin,fill=name))+
geom_violin()## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
ridge plot
weather_df %>%
ggplot(aes(x=tmin,y=name,fill=name))+
geom_density_ridges()## Picking joint bandwidth of 1.41## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_density_ridges()`).
LA univariate plots Learning assessment: Make plots that compare precipitation across locaations. Try a histogram, a density plot, a boxplot, a violin plot, and a ridgeplot; use aesthetic mappings to make your figure readable
Density Plot
weather_df %>%
filter(prcp>5, prcp<1000) %>%
ggplot(aes(x=prcp,fill=name))+
geom_density()
weather_df %>%
ggplot(aes(x=prcp,fill=name))+
geom_density()## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_density()`).
Ridge plot
weather_df %>%
ggplot(aes(x=prcp,y=name,fill=name))+
geom_density_ridges(scale=0.85)## Picking joint bandwidth of 9.22## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_density_ridges()`).
Boxplot
weather_df %>%
ggplot(aes(y=prcp,x=name))+
geom_boxplot()## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
##saving and embedding plots
saving plots
ggp_weather_violin=
weather_df %>%
ggplot(aes(x=name,y=tmin,fill=name))+
geom_violin()
ggp_weather_violin## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
ggsave("plots/violin_plot.pdf",ggp_weather_violin,
width=8,height=6)## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_ydensity()`).